OpenRaiser/Intern-Atlas huggingface dataset:
Deep Learning for Multiscale Damage Analysis via Physics-Informed Recurrent Neural Network
Venue: arXiv cs.CE 2022
Year: 2022

Direct numerical simulation of hierarchical materials via homogenization-based concurrent multiscale models poses critical challenges for 3D large scale engineering applications, as the computation of highly nonlinear and path-dependent material constitutive responses at the lower scale causes prohibitively high computational costs. In this work, we propose a physics-informed data-driven deep learning model as an efficient surrogate to emulate the effective responses of heterogeneous microstructures under irreversible elasto-plastic hardening and softening deformation. Our contribution contains several major innovations. First, we propose a novel training scheme to generate arbitrary loading sequences in the sampling space confined by deformation constraints where the simulation cost of homogenizing microstructural responses per sequence is dramatically reduced via mechanistic reduced-order models. Second, we develop a new sequential learner that incorporates thermodynamics consistent physics constraints by customizing training loss function and data flow architecture. We additionally demonstrate the integration of trained surrogate within the framework of classic multiscale finite element solver. Our numerical experiments indicate that our model shows a significant accuracy improvement over pure data-driven emulator and a dramatic efficiency boost than reduced models. We believe our data-driven model provides a computationally efficient and mechanics consistent alternative for classic constitutive laws beneficial for potential high-throughput simulations that needs material homogenization of irreversible behaviors.
A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center
Venue: arXiv cs.DC 2022
Year: 2022

This work proposes an energy-efficient resource provisioning and allocation framework to meet the dynamic demands of future applications. The frequent variations in a cloud user's resource demand lead 'to the problem of excess power consumption, resource wastage, performance, and Quality-of-Service degradation. The proposed framework addresses these challenges by matching the application's predicted resource requirement with the resource capacity of VMs precisely and thereby consolidating the entire load on the minimum number of energy-efficient physical machines. The three consecutive contributions of the proposed work are: Online Multi-Resource Feed-forward Neural Network to forecast the multiple resource demands concurrently for future applications; autoscaling of VMs based on the clustering of the predicted resource requirements; allocation of the scaled VMs on the energy-efficient PMs. The integrated approach successively optimizes resource utilization, saves energy and automatically adapts to the changes in future application resource demand. The proposed framework is evaluated by using real workload traces of the benchmark Google Cluster Dataset and compared against different scenarios including energy-efficient VM placement with resource prediction only, VMP without resource prediction and autoscaling, and optimal VMP with autoscaling based on actual resource utilization. The observed results demonstrate that the proposed integrated approach achieves near-optimal performance against optimal VMP and outperforms rest of the VMPs in terms of power saving and resource utilization up to 88.5% and 21.12% respectively. In addition, the OM-FNN predictor shows better accuracy, lesser time and space complexity over a traditional single-input and single-output feed-forward neural network predictor.
Combining multiple matchers for fingerprint verification: A case study in biosecure network of excellence
Venue: arXiv cs.CV 2022
Year: 2022

We report on experiments for the fingerprint modality conducted during the First BioSecure Residential Workshop. Two reference systems for fingerprint verification have been tested together with two additional non-reference systems. These systems follow different approaches of fingerprint processing and are discussed in detail. Fusion experiments I volving different combinations of the available systems are presented. The experimental results show that the best recognition strategy involves both minutiae-based and correlation-based measurements. Regarding the fusion experiments, the best relative improvement is obtained when fusing systems that are based on heterogeneous strategies for feature extraction and/or matching. The best combinations of two/three/four systems always include the best individual systems whereas the best verification performance is obtained when combining all the available systems.
Computing a Dirichlet domain for a hyperbolic surface
Venue: arXiv cs.CG 2022
Year: 2022

The goal of this paper is to exhibit and analyze an algorithm that takes a given closed orientable hyperbolic surface and outputs an explicit Dirichlet domain. The input is a fundamental polygon with side pairings. While grounded in topological considerations, the algorithm makes key use of the geometry of the surface. We introduce data structures that reflect this interplay between geometry and topology and show that the algorithm finishes in polynomial time, in terms of the initial perimeter and the genus of the surface.
Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transport
Venue: arXiv cs.RO 2022
Year: 2022

In this paper, we present a solution to a design problem of control strategies for multi-agent cooperative transport. Although existing learning-based methods assume that the number of agents is the same as that in the training environment, the number might differ in reality considering that the robots' batteries may completely discharge, or additional robots may be introduced to reduce the time required to complete a task. Therefore, it is crucial that the learned strategy be applicable to scenarios wherein the number of agents differs from that in the training environment. In this paper, we propose a novel multi-agent reinforcement learning framework of event-triggered communication and consensus-based control for distributed cooperative transport. The proposed policy model estimates the resultant force and torque in a consensus manner using the estimates of the resultant force and torque with the neighborhood agents. Moreover, it computes the control and communication inputs to determine when to communicate with the neighboring agents under local observations and estimates of the resultant force and torque. Therefore, the proposed framework can balance the control performance and communication savings in scenarios wherein the number of agents differs from that in the training environment. We confirm the effectiveness of our approach by using a maximum of eight and six robots in the simulations and experiments, respectively.
Dissimilar Nodes Improve Graph Active Learning
Venue: arXiv cs.LG 2022
Year: 2022

Training labels for graph embedding algorithms could be costly to obtain in many practical scenarios. Active learning (AL) algorithms are very helpful to obtain the most useful labels for training while keeping the total number of label queries under a certain budget. The existing Active Graph Embedding framework proposes to use centrality score, density score, and entropy score to evaluate the value of unlabeled nodes, and it has been shown to be capable of bringing some improvement to the node classification tasks of Graph Convolutional Networks. However, when evaluating the importance of unlabeled nodes, it fails to consider the influence of existing labeled nodes on the value of unlabeled nodes. In other words, given the same unlabeled node, the computed informative score is always the same and is agnostic to the labeled node set. With the aim to address this limitation, in this work, we introduce 3 dissimilarity-based information scores for active learning: feature dissimilarity score (FDS), structure dissimilarity score (SDS), and embedding dissimilarity score (EDS). We find out that those three scores are able to take the influence of the labeled set on the value of unlabeled candidates into consideration, boosting our AL performance. According to experiments, our newly proposed scores boost the classification accuracy by 2.1% on average and are capable of generalizing to different Graph Neural Network architectures.
Exploring Stroke-Level Modifications for Scene Text Editing
Venue: arXiv cs.CV 2022
Year: 2022

Scene text editing (STE) aims to replace text with the desired one while preserving background and styles of the original text. However, due to the complicated background textures and various text styles, existing methods fall short in generating clear and legible edited text images. In this study, we attribute the poor editing performance to two problems: 1) Implicit decoupling structure. Previous methods of editing the whole image have to learn different translation rules of background and text regions simultaneously. 2) Domain gap. Due to the lack of edited real scene text images, the network can only be well trained on synthetic pairs and performs poorly on real-world images. To handle the above problems, we propose a novel network by MOdifying Scene Text image at strokE Level (MOSTEL). Firstly, we generate stroke guidance maps to explicitly indicate regions to be edited. Different from the implicit one by directly modifying all the pixels at image level, such explicit instructions filter out the distractions from background and guide the network to focus on editing rules of text regions. Secondly, we propose a Semi-supervised Hybrid Learning to train the network with both labeled synthetic images and unpaired real scene text images. Thus, the STE model is adapted to real-world datasets distributions. Moreover, two new datasets (Tamper-Syn2k and Tamper-Scene) are proposed to fill the blank of public evaluation datasets. Extensive experiments demonstrate that our MOSTEL outperforms previous methods both qualitatively and quantitatively. Datasets and code will be available at https://github.com/qqqyd/MOSTEL.
DA-CIL: Towards Domain Adaptive Class-Incremental 3D Object Detection
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning has achieved notable success in 3D object detection with the advent of large-scale point cloud datasets. However, severe performance degradation in the past trained classes, i.e., catastrophic forgetting, still remains a critical issue for real-world deployment when the number of classes is unknown or may vary. Moreover, existing 3D class-incremental detection methods are developed for the single-domain scenario, which fail when encountering domain shift caused by different datasets, varying environments, etc. In this paper, we identify the unexplored yet valuable scenario, i.e., class-incremental learning under domain shift, and propose a novel 3D domain adaptive class-incremental object detection framework, DA-CIL, in which we design a novel dual-domain copy-paste augmentation method to construct multiple augmented domains for diversifying training distributions, thereby facilitating gradual domain adaptation. Then, multi-level consistency is explored to facilitate dual-teacher knowledge distillation from different domains for domain adaptive class-incremental learning. Extensive experiments on various datasets demonstrate the effectiveness of the proposed method over baselines in the domain adaptive class-incremental learning scenario.
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition
Venue: arXiv cs.CV 2022
Year: 2022

The Weather4Cast competition (hosted by NeurIPS 2022) required competitors to predict super-resolution rain movies in various regions of Europe when low-resolution satellite contexts covering wider regions are given. In this paper, we show that a general baseline 3D U-Net can be significantly improved with region-conditioned layers as well as orthogonality regularizations on 1x1x1 convolutional layers. Additionally, we facilitate the generalization with a bag of training strategies: mixup data augmentation, self-distillation, and feature-wise linear modulation (FiLM). Presented modifications outperform the baseline algorithms (3D U-Net) by up to 19.54% with less than 1% additional parameters, which won the 4th place in the core test leaderboard.
AMORETTO: A Method for Deriving IoT-enriched Event Logs
Venue: arXiv cs.DB 2022
Year: 2022

Process analytics aims to gain insights into the behaviour and performance of business processes through the analysis of event logs, which record the execution of processes. With the widespread use of the Internet of Things (IoT), IoT data has become readily available and can provide valuable context information about business processes. As such, process analytics can benefit from incorporating IoT data into event logs to support more comprehensive, context-aware analyses. However, most existing studies focus on enhancing business process models with IoT data, whereas little attention has been paid to incorporating IoT data into event logs for process analytics. Hence, this paper aims to systematically integrate IoT data into event logs to support context-aware process analytics. To this end, we propose AMORETTO - a method for deriving IoT-enriched event logs. Firstly, we provide a classification of context data, referred to as the IoT-Pro context classification, which encompasses two context dimensions: IoT context and process context. Next, we present a method for integrating IoT data with event logs, guided by IoT-Pro, to yield IoT-enriched event logs. To demonstrate the applicability of AMORETTO, we applied it to a real-life use case and examined whether the derived IoT-enriched event log sufficed to address certain specific analytical questions.
Hierarchical Contrast for Unsupervised Skeleton-based Action Representation Learning
Venue: arXiv cs.CV 2022
Year: 2022

This paper targets unsupervised skeleton-based action representation learning and proposes a new Hierarchical Contrast (HiCo) framework. Different from the existing contrastive-based solutions that typically represent an input skeleton sequence into instance-level features and perform contrast holistically, our proposed HiCo represents the input into multiple-level features and performs contrast in a hierarchical manner. Specifically, given a human skeleton sequence, we represent it into multiple feature vectors of different granularities from both temporal and spatial domains via sequence-to-sequence (S2S) encoders and unified downsampling modules. Besides, the hierarchical contrast is conducted in terms of four levels: instance level, domain level, clip level, and part level. Moreover, HiCo is orthogonal to the S2S encoder, which allows us to flexibly embrace state-of-the-art S2S encoders. Extensive experiments on four datasets, i.e., NTU-60, NTU-120, PKU-MMD I and II, show that HiCo achieves a new state-of-the-art for unsupervised skeleton-based action representation learning in two downstream tasks including action recognition and retrieval, and its learned action representation is of good transferability. Besides, we also show that our framework is effective for semi-supervised skeleton-based action recognition. Our code is available at https://github.com/HuiGuanLab/HiCo.
Minimum Class Confusion based Transfer for Land Cover Segmentation in Rural and Urban Regions
Venue: arXiv cs.CV 2022
Year: 2022

Transfer Learning methods are widely used in satellite image segmentation problems and improve performance upon classical supervised learning methods. In this study, we present a semantic segmentation method that allows us to make land cover maps by using transfer learning methods. We compare models trained in low-resolution images with insufficient data for the targeted region or zoom level. In order to boost performance on target data we experiment with models trained with unsupervised, semi-supervised and supervised transfer learning approaches, including satellite images from public datasets and other unlabeled sources. According to experimental results, transfer learning improves segmentation performance 3.4% MIoU (Mean Intersection over Union) in rural regions and 12.9% MIoU in urban regions. We observed that transfer learning is more effective when two datasets share a comparable zoom level and are labeled with identical rules; otherwise, semi-supervised learning is more effective by using the data as unlabeled. In addition, experiments showed that HRNet outperformed building segmentation approaches in multi-class segmentation.
Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning
Venue: arXiv cs.NI 2022
Year: 2022

Data generated at the network edge can be processed locally by leveraging the paradigm of edge computing (EC). Aided by EC, decentralized federated learning (DFL), which overcomes the single-point-of-failure problem in the parameter server (PS) based federated learning, is becoming a practical and popular approach for machine learning over distributed data. However, DFL faces two critical challenges, \ie, system heterogeneity and statistical heterogeneity introduced by edge devices. To ensure fast convergence with the existence of slow edge devices, we present an efficient DFL method, termed FedHP, which integrates adaptive control of both local updating frequency and network topology to better support the heterogeneous participants. We establish a theoretical relationship between local updating frequency and network topology regarding model training performance and obtain a convergence upper bound. Upon this, we propose an optimization algorithm, that adaptively determines local updating frequencies and constructs the network topology, so as to speed up convergence and improve the model accuracy. Evaluation results show that the proposed FedHP can reduce the completion time by about 51% and improve model accuracy by at least 5% in heterogeneous scenarios, compared with the baselines.
Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain
Venue: arXiv cs.CL 2022
Year: 2022

Multilingual Neural Machine Translation (MNMT) models leverage many language pairs during training to improve translation quality for low-resource languages by transferring knowledge from high-resource languages. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation which includes terminology-specific error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer terminology errors.
Low-Resolution Horizontal and Vertical Layered Mutual Information Maximizing LDPC Decoding
Venue: arXiv cs.IT 2022
Year: 2022

We investigate iterative low-resolution message-passing algorithms for quasi-cyclic LDPC codes with horizontal and vertical layered schedules. Coarse quantization and layered scheduling are highly relevant for hardware implementations to reduce the bit width of messages and the number of decoding iterations. As a novelty, this paper compares the two scheduling variants in combination with mutual information maximizing compression operations in variable and check nodes. We evaluate the complexity and error rate performance for various configurations. Dedicated hardware architectures for regular quasi-cyclic LDPC decoders are derived on a conceptual level. The hardware-resource estimates confirm that most of the complexity lies within the routing network operations. Our simulations reveal similar error rate performance for both layered schedules but a slightly lower average iteration count for the horizontal decoder.
WAIR-D: Wireless AI Research Dataset
Venue: arXiv cs.LG 2022
Year: 2022

It is a common sense that datasets with high-quality data samples play an important role in artificial intelligence (AI), machine learning (ML) and related studies. However, although AI/ML has been introduced in wireless researches long time ago, few datasets are commonly used in the research community. Without a common dataset, AI-based methods proposed for wireless systems are hard to compare with both the traditional baselines and even each other. The existing wireless AI researches usually rely on datasets generated based on statistical models or ray-tracing simulations with limited environments. The statistical data hinder the trained AI models from further fine-tuning for a specific scenario, and ray-tracing data with limited environments lower down the generalization capability of the trained AI models. In this paper, we present the Wireless AI Research Dataset (WAIR-D)1, which consists of two scenarios. Scenario 1 contains 10,000 environments with sparsely dropped user equipments (UEs), and Scenario 2 contains 100 environments with densely dropped UEs. The environments are randomly picked up from more than 40 cities in the real world map. The large volume of the data guarantees that the trained AI models enjoy good generalization capability, while fine-tuning can be easily carried out on a specific chosen environment. Moreover, both the wireless channels and the corresponding environmental information are provided in WAIR-D, so that extra-information-aided communication mechanism can be designed and evaluated. WAIR-D provides the researchers benchmarks to compare their different designs or reproduce results of others. In this paper, we show the detailed construction of this dataset and examples of using it.
3D-LatentMapper: View Agnostic Single-View Reconstruction of 3D Shapes
Venue: arXiv cs.CV 2022
Year: 2022

Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to represent and generate 3D shapes, as well as a vast number of use cases. However, single-view reconstruction remains a challenging topic that can unlock various interesting use cases such as interactive design. In this work, we propose a novel framework that leverages the intermediate latent spaces of Vision Transformer (ViT) and a joint image-text representational model, CLIP, for fast and efficient Single View Reconstruction (SVR). More specifically, we propose a novel mapping network architecture that learns a mapping between deep features extracted from ViT and CLIP, and the latent space of a base 3D generative model. Unlike previous work, our method enables view-agnostic reconstruction of 3D shapes, even in the presence of large occlusions. We use the ShapeNetV2 dataset and perform extensive experiments with comparisons to SOTA methods to demonstrate our method's effectiveness.
FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views
Venue: arXiv cs.CV 2022
Year: 2022

Federated Deep Learning frameworks can be used strategically to monitor Land Use locally and infer environmental impacts globally. Distributed data from across the world would be needed to build a global model for Land Use classification. The need for a Federated approach in this application domain would be to avoid transfer of data from distributed locations and save network bandwidth to reduce communication cost. We use a Federated UNet model for Semantic Segmentation of satellite and street view images. The novelty of the proposed architecture is the integration of Knowledge Distillation to reduce communication cost and response time. The accuracy obtained was above 95% and we also brought in a significant model compression to over 17 times and 62 times for street View and satellite images respectively. Our proposed framework has the potential to be a game-changer in real-time tracking of climate change across the planet.